Job description

Position: Data Engineer

Location: Pune

We are seeking a visionary and technically skilled Data Engineer to design, implement, and optimize scalable machine learning infrastructure and operations. You will work closely with data scientists, analysts, and other stakeholders to ensure data availability and quality. Your expertise in AWS, Python, and Spark will be crucial in building scalable data pipelines and processing large datasets efficiently. Experience with NoSQL databases, particularly MarkLogic, and full stack development is a plus.

About ACS-I India:

ACS International India Pvt Ltd. (ACSI India) is a wholly owned subsidiary of ACS International Ltd, USA and a part of the American Chemical Society. ACSI India represent products and services provided by ACS divisions, including Chemical Abstracts Service (CAS) to the world’s most important scientific companies, government organizations, global patent offices and academic institutions to promote research and discovery.

About CAS:

Chemical Abstracts Service is a division of the American Chemical Society. It is a source of chemical information. The Company provides products and services, solutions for researchers and professional searchers, and support and training. Chemical Abstracts service has provided the most comprehensive repository of research in chemistry and related sciences for over 100 years. The CAS finds, collects and organizes all publicly disclosed substance information and creates the world's most valuable chemistry databases. Scientist and patent professionals across the world rely on this database.

Job Responsibilities:

Design, develop, and maintain scalable data pipelines using AWS services.
Design and implement scalable, secure, and resilient MLOps architecture across cloud and on-prem environments
Define best practices for model lifecycle management, including versioning, deployment, monitoring, and governance
Lead the selection and integration of tools for CI/CD, model registry, feature store, and data pipelines
Automate workflows for data ingestion, preprocessing, training, validation, and deployment
Establish monitoring and alerting systems for model performance, data drift, and infrastructure health
Ensure reproducibility, traceability, and compliance with regulatory standards
Work closely with data scientists, data engineers, software developers, and DevOps teams
Mentor junior engineers and promote a culture of continuous learning and innovation
Translate business requirements into scalable AI/ML solutions
Evaluate emerging technologies and frameworks to enhance MLOps capabilities

Job Requirements:

Bachelor’s/master’s in computer science, Data Engineering, AI/ML, or related field
4+ years of experience in software engineering, data engineering, or ML engineering
3+ years of hands-on experience in MLOps or ML infrastructure roles.
Expertise in cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes)
Proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps tools (MLflow, Kubeflow, Airflow)
Strong programming skills in Python, Bash, and familiarity with infrastructure-as-code (Terraform, CloudFormation)
Experience with monitoring tools (Prometheus, Grafana) and logging systems (ELK stack, Fluentd)

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